Regression Models as a Tool in Medical Research

Regression Models as a Tool in Medical Research is a comprehensive guide for beginners to understand and apply regression models in medical research. It covers basic concepts, popular models, advanced topics, and techniques, with a focus on proper use, interpretation, and presentation of results. The book also discusses the comparison of regression coefficients, selection of covariates, modeling of nonlinear and nonadditive effects, and analysis of clustered and longitudinal data. It provides an overview of risk scores and predictors, as well as more specific regression models and their applications.

Format: Paperback / softback
Length: 496 pages
Publication date: 21 January 2023
Publisher: Taylor & Francis Ltd

Regression models have become indispensable tools in medical research, offering valuable insights into various aspects of health and disease. However, the complexity of these models and the need for proper interpretation can pose challenges for beginners in the field. To address this, Regression Models as a Tool in Medical Research provides a comprehensive and accessible guide to the world of regression models.

The book begins by introducing the fundamental concepts and important aspects of regression models, including the classical regression model for continuous outcomes, the logistic regression model for binary outcomes, and the Cox proportional hazards model for survival data. It emphasizes the importance of adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls.

After reviewing popular models and basic methods, the book delves into advanced topics and techniques. It explores the comparison of regression coefficients, the selection of covariates, the modeling of nonlinear and nonadditive effects, and the analysis of clustered and longitudinal data. It highlights the impact of selection mechanisms, measurement error, and incomplete covariate data on the accuracy and reliability of regression models.

Furthermore, the text covers the use of regression models to construct risk scores and predictors. It provides an overview of more specific regression models and their applications, as well as alternatives to regression modeling. The mathematical details underlying the estimation and inference techniques are presented in the appendices, making the book accessible to a wide range of readers, including those with limited mathematical backgrounds.

In conclusion, Regression Models as a Tool in Medical Research is an essential resource for anyone interested in applying regression models in medical research. It offers a comprehensive and accessible guide to the world of regression models, covering both basic and advanced topics. By providing detailed explanations, examples, and practical insights, the book empowers researchers to make informed decisions based on data and improve our understanding of health and disease.

Weight: 920g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032477510